add save/load test

README.md

@@ -123,6 +123,11 @@ iris_nn <- hnsw_search(irism[101:150, ], ann, k = 5)
 
 ## Class Example
 
+As noted in the "Do not use named parameters" section below, you should avoid
+using named parameters when using class methods. But I do use them in a few
+places below to document the name of the parameters the positional arguments
+refer to.
+
 ```R
 library(RcppHNSW)
 data <- as.matrix(iris[, -5])
@@ -153,7 +158,7 @@ res <- ann$getNNsList(data[1, ], k = 4, include_distances = TRUE)
 ann2 <- new(HnswL2, dim, nitems, M, ef)
 ann2$addItems(data)
 # Retrieve the 4 nearest neighbors for every item in data
-res2 <- ann2$getAllNNsList(data, k = 4, include_distances = TRUE)
+res2 <- ann2$getAllNNsList(data, 4, TRUE)
 # labels of the data are in res$item, distances in res$distance
 
 # If you are able to store your data column-wise, then the overhead of copying
@@ -162,10 +167,18 @@ data_by_col <- t(data)
 ann3 <- new(HnswL2, dim, nitems, M, ef)
 ann3$addItemsCol(data_by_col)
 # Retrieve the 4 nearest neighbors for every item in data_by_col
-res3 <- ann3$getAllNNsListCol(data_by_col, k = 4, include_distances = TRUE)
+res3 <- ann3$getAllNNsListCol(data_by_col, 4, TRUE)
 # The returned neared neighbor data matrices are also returned column-wise
 all(res2$item == t(res3$item) & res2$distance == t(res3$distance))
 
+# Save the index
+ann$save("iris.hnsw")
+
+# load it back in: you do need to know the dimension of the original data
+ann4 <- new(HnswL2, dim, "iris.hnsw")
+# new index should behave like the original
+all(ann$getNNs(data[1, ], 4) == ann4$getNNs(data[1, ], 4))
+
 # other distance classes:
 # Cosine: HnswCosine
 # Inner Product: HnswIP

tests/testthat/test_save_load.R

@@ -0,0 +1,47 @@
+library(RcppHNSW)
+context("Save/load index")
+
+num_elements <- nrow(uirism)
+dim <- ncol(uirism)
+
+M <- 16
+ef_construction <- 10
+p <- new(HnswL2, dim, num_elements, M, ef_construction)
+
+for (i in 1:num_elements) {
+  p$addItem(uirism[i, ])
+}
+
+nn4idx <- matrix(0L, nrow = num_elements, ncol = 4)
+nn4dist <- matrix(0.0, nrow = num_elements, ncol = 4)
+
+for (i in 1:num_elements) {
+  res <- p$getNNsList(uirism[i, ], k = 4, TRUE)
+  nn4idx[i, ] <- res$item
+  nn4dist[i, ] <- res$distance
+}
+
+temp_file <- tempfile()
+on.exit(unlink(temp_file), add = TRUE)
+p$save(temp_file)
+
+nn4idx_aftersave <- matrix(0L, nrow = num_elements, ncol = 4)
+nn4dist_aftersave <- matrix(0.0, nrow = num_elements, ncol = 4)
+for (i in 1:num_elements) {
+  res_aftersave <- p$getNNsList(uirism[i, ], k = 4, TRUE)
+  nn4idx_aftersave[i, ] <- res_aftersave$item
+  nn4dist_aftersave[i, ] <- res_aftersave$distance
+}
+expect_equal(nn4idx, nn4idx_aftersave)
+expect_equal(nn4dist, nn4dist_aftersave)
+
+pload <- new(HnswL2, dim, temp_file)
+nn4idx_afterload <- matrix(0L, nrow = num_elements, ncol = 4)
+nn4dist_afterload <- matrix(0.0, nrow = num_elements, ncol = 4)
+for (i in 1:num_elements) {
+  res_afterload <- pload$getNNsList(uirism[i, ], k = 4, TRUE)
+  nn4idx_afterload[i, ] <- res_afterload$item
+  nn4dist_afterload[i, ] <- res_afterload$distance
+}
+expect_equal(nn4idx, nn4idx_afterload)
+expect_equal(nn4dist, nn4dist_afterload)